Galaxy General, Tsinghua, and collaborators propose LATENT, teaching humanoids tennis from imperfect human data
银河通用&清华 LATENT：用「不完美人类数据」让机器人学会网球对打 | IROS 2026
What happened
The GaLbot team at Galaxy General, together with Tsinghua, Peking University, and others, proposed LATENT, which trains humanoid robots to play tennis using about five hours of imperfect motion data from five amateur players. The method first trains a motion tracker and compresses it into a latent action space with a Latent Action Barrier constraint. On a Unitree G1, it achieved a 90.90% success rate on real forehands and 77.78% on backhands.
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